---
title: Enhancing a Multi-Factor Equity Strategy via Artificial Intelligence and Technical Indicators
url: https://www.ml-quant.com/papers/repec/cfi-fseres-cf632/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-11-14
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:cfi:fseres:cf632
source_url: https://econpapers.repec.org/RePEc:cfi:fseres:cf632
featured: 2026-11-13
citations: unknown
topic: ML & AI Methods
---


# Enhancing a Multi-Factor Equity Strategy via Artificial Intelligence and Technical Indicators

Combining a five-factor model with gradient boosting and technical indicators for hedging, the strategy outperforms index buy-and-hold on Japanese stocks while maintaining interpretability.

- Source: https://econpapers.repec.org/RePEc:cfi:fseres:cf632
- Identifier: RePEc:cfi:fseres:cf632
- Released: 2026-11-09
- First featured: Quant Letter No. 139 (2026-11-13): https://www.ml-quant.com/issues/2026-11-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods
- Authors: Daiya Mita, Akihiko Takahashi

## Related

- [Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders](https://www.ml-quant.com/papers/arxiv/2407.14435/): The paper presents JumpReLU Sparse Autoencoders (SAEs), which provide high-quality reconstruction of language model activations at a specific sparsity level, while maintaining interpretability.
- [Neuro-Symbolic AI in 2024: A Systematic Review](https://www.ml-quant.com/papers/arxiv/2501.05435/): Neuro-Symbolic AI has grown since 2020, focusing on learning and inference, but still lacks in areas like explainability, trustworthiness, and Meta-Cognition.
- [Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review](https://www.ml-quant.com/papers/arxiv/2502.00201/): The study examines progress in deep learning methods for detecting financial fraud, reviewing 57 studies from 2019 to 2024, and discusses challenges and opportunities such as data privacy, feature engineering, and model interpretability.
- [The Impact of Imperfect XAI on Human-AI Decision-Making](https://www.ml-quant.com/papers/doi/10-1145-3641022/): A study shows that incorrect explanations from explainable AI (XAI) can influence human decision-making, with the impact varying based on the level of human expertise and the quality of the AI.
- [Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science](https://www.ml-quant.com/papers/arxiv/2303.00652/): The paper evaluates different explainable artificial intelligence methods in the context of climate science, comparing their suitability for specific research problems.
- [Identifying regions of importance in wall-bounded turbulence through explainable deep learning](https://www.ml-quant.com/papers/arxiv/2302.01250/): A deep-learning method has been used to analyze energy structures in turbulence, finding that the most significant structures aren't always the ones contributing most to Reynolds shear stress.